UXarray for Advanced HEALPix Analysis & Visualization
In this section, you’ll learn:
Using the
uxarraypackage to perform advanced analysis operators over HEALPix data such as non-conservative zonal means, etc.
Related Documentation
Prerequisites
Concepts |
Importance |
Notes |
|---|---|---|
Necessary |
||
Necessary |
Time to learn: 30 minutes
import uxarray as ux
import cartopy.crs as ccrs
Open data catalog
Tip
We assume, you have already gone over the previous section, UXarray for Basic HEALPix Statistics & Visualization. If not and if you need to learn about data catalogs in general and the data we will use throughout this notebook, we recommend to check that section first.:::
Let us open the online catalog from the WCRP’s Digital Earths Global Hackathon 2025 using intake and read the output of the ICON run ngc4008, which is stored in the HEALPix format:
import intake
# Final data catalog location (once hackathon website (https://digital-earths-global-hackathon.github.io/) updated)
#cat_url='https://digital-earths-global-hackathon.github.io/catalog/catalog.yaml'
# Interim data catalog location
cat_url='https://raw.githubusercontent.com/digital-earths-global-hackathon/catalog/refs/heads/ncar/online/main.yaml'
cat = intake.open_catalog(cat_url)
model_run = cat.icon_ngc4008
We can look into a fine resolution dataset at zoome level = 10 in it as Xarray.Dataset:
ds = model_run(zoom=9, time="P1D").to_dask()
ds
/home/runner/miniconda3/envs/healpix-cookbook-dev/lib/python3.10/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
'dims': dict(self._ds.dims),
<xarray.Dataset> Size: 232TB
Dimensions: (time: 10958, depth_half: 73,
cell: 3145728, level_full: 90,
crs: 1, depth_full: 72,
soil_depth_water_level: 5,
level_half: 91,
soil_depth_energy_level: 5)
Coordinates:
* crs (crs) float32 4B nan
* depth_full (depth_full) float32 288B 1.0 ... 5....
* depth_half (depth_half) float32 292B 0.0 ... 5....
* level_full (level_full) int32 360B 1 2 3 ... 89 90
* level_half (level_half) int32 364B 1 2 3 ... 90 91
* soil_depth_energy_level (soil_depth_energy_level) float32 20B ...
* soil_depth_water_level (soil_depth_water_level) float32 20B ...
* time (time) datetime64[ns] 88kB 2020-01-0...
Dimensions without coordinates: cell
Data variables: (12/103)
A_tracer_v_to (time, depth_half, cell) float32 10TB ...
FrshFlux_IceSalt (time, cell) float32 138GB ...
FrshFlux_TotalIce (time, cell) float32 138GB ...
Qbot (time, cell) float32 138GB ...
Qtop (time, cell) float32 138GB ...
Wind_Speed_10m (time, cell) float32 138GB ...
... ...
vas (time, cell) float32 138GB ...
w (time, depth_half, cell) float32 10TB ...
wa_phy (time, level_half, cell) float32 13TB ...
zg (level_full, cell) float32 1GB ...
zghalf (level_half, cell) float32 1GB ...
zos (time, cell) float32 138GB ...- time: 10958
- depth_half: 73
- cell: 3145728
- level_full: 90
- crs: 1
- depth_full: 72
- soil_depth_water_level: 5
- level_half: 91
- soil_depth_energy_level: 5
- crs(crs)float32nan
- grid_mapping_name :
- healpix
- healpix_nside :
- 512
- healpix_order :
- nest
array([nan], dtype=float32)
- depth_full(depth_full)float321.0 3.1 ... 5.546e+03 5.816e+03
- axis :
- Z
- long_name :
- depth_below_sea
- positive :
- down
- standard_name :
- depth
- units :
- m
array([1.00000e+00, 3.10000e+00, 5.45000e+00, 8.10000e+00, 1.10500e+01, 1.43500e+01, 1.80500e+01, 2.22000e+01, 2.68500e+01, 3.20000e+01, 3.76500e+01, 4.38000e+01, 5.05500e+01, 5.79500e+01, 6.60000e+01, 7.48000e+01, 8.44500e+01, 9.50000e+01, 1.06500e+02, 1.19100e+02, 1.32900e+02, 1.47950e+02, 1.64350e+02, 1.82250e+02, 2.01800e+02, 2.23150e+02, 2.46450e+02, 2.71850e+02, 2.99550e+02, 3.29750e+02, 3.62650e+02, 3.98450e+02, 4.37400e+02, 4.79250e+02, 5.23450e+02, 5.69500e+02, 6.17100e+02, 6.66300e+02, 7.17150e+02, 7.69700e+02, 8.24000e+02, 8.80100e+02, 9.38050e+02, 9.97900e+02, 1.05975e+03, 1.12450e+03, 1.19410e+03, 1.27070e+03, 1.35630e+03, 1.45170e+03, 1.55680e+03, 1.67195e+03, 1.79645e+03, 1.92950e+03, 2.07140e+03, 2.22245e+03, 2.38290e+03, 2.55290e+03, 2.73250e+03, 2.92175e+03, 3.12060e+03, 3.32885e+03, 3.54625e+03, 3.77245e+03, 4.00695e+03, 4.24915e+03, 4.49830e+03, 4.75355e+03, 5.01400e+03, 5.27860e+03, 5.54625e+03, 5.81575e+03], dtype=float32) - depth_half(depth_half)float320.0 2.0 4.2 ... 5.681e+03 5.951e+03
- axis :
- Z
- long_name :
- depth_below_sea
- positive :
- down
- standard_name :
- depth
- units :
- m
array([0.0000e+00, 2.0000e+00, 4.2000e+00, 6.7000e+00, 9.5000e+00, 1.2600e+01, 1.6100e+01, 2.0000e+01, 2.4400e+01, 2.9300e+01, 3.4700e+01, 4.0600e+01, 4.7000e+01, 5.4100e+01, 6.1800e+01, 7.0200e+01, 7.9400e+01, 8.9500e+01, 1.0050e+02, 1.1250e+02, 1.2570e+02, 1.4010e+02, 1.5580e+02, 1.7290e+02, 1.9160e+02, 2.1200e+02, 2.3430e+02, 2.5860e+02, 2.8510e+02, 3.1400e+02, 3.4550e+02, 3.7980e+02, 4.1710e+02, 4.5770e+02, 5.0080e+02, 5.4610e+02, 5.9290e+02, 6.4130e+02, 6.9130e+02, 7.4300e+02, 7.9640e+02, 8.5160e+02, 9.0860e+02, 9.6750e+02, 1.0283e+03, 1.0912e+03, 1.1578e+03, 1.2304e+03, 1.3110e+03, 1.4016e+03, 1.5018e+03, 1.6118e+03, 1.7321e+03, 1.8608e+03, 1.9982e+03, 2.1446e+03, 2.3003e+03, 2.4655e+03, 2.6403e+03, 2.8247e+03, 3.0188e+03, 3.2224e+03, 3.4353e+03, 3.6572e+03, 3.8877e+03, 4.1262e+03, 4.3721e+03, 4.6245e+03, 4.8826e+03, 5.1454e+03, 5.4118e+03, 5.6807e+03, 5.9508e+03], dtype=float32) - level_full(level_full)int321 2 3 4 5 6 7 ... 85 86 87 88 89 90
- axis :
- Z
- long_name :
- generalized_height
- positive :
- down
- standard_name :
- height
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90], dtype=int32) - level_half(level_half)int321 2 3 4 5 6 7 ... 86 87 88 89 90 91
- axis :
- Z
- long_name :
- generalized_height
- positive :
- down
- standard_name :
- height
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91], dtype=int32) - soil_depth_energy_level(soil_depth_energy_level)float320.0325 0.192 0.7755 2.683 6.984
- axis :
- Z
- long_name :
- depth_below_land
- positive :
- down
- units :
- m
array([0.0325, 0.192 , 0.7755, 2.683 , 6.984 ], dtype=float32)
- soil_depth_water_level(soil_depth_water_level)float320.0325 0.192 0.7755 2.683 6.984
- axis :
- Z
- long_name :
- depth_below_land
- positive :
- down
- units :
- m
array([0.0325, 0.192 , 0.7755, 2.683 , 6.984 ], dtype=float32)
- time(time)datetime64[ns]2020-01-02 ... 2050-01-01
- axis :
- T
array(['2020-01-02T00:00:00.000000000', '2020-01-03T00:00:00.000000000', '2020-01-04T00:00:00.000000000', ..., '2049-12-30T00:00:00.000000000', '2049-12-31T00:00:00.000000000', '2050-01-01T00:00:00.000000000'], shape=(10958,), dtype='datetime64[ns]')
- A_tracer_v_to(time, depth_half, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- sea water vertical diffusivity
- standard_name :
- ocean_vertical_diffusivity
- units :
- m2 s-1
- vgrid :
- depth_below_sea_half
[2516374781952 values with dtype=float32]
- FrshFlux_IceSalt(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Salt volume flux due to sea ice change
- units :
- psu m s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- FrshFlux_TotalIce(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Freshwater Flux due to Sea Ice Change
- units :
- m s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- Qbot(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Conductive heat flux at ice-ocean interface
- units :
- W/m^2
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- Qtop(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Energy flux available for surface melting
- units :
- W/m^2
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- Wind_Speed_10m(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Wind Speed at 10m height
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_FrshFlux_Evaporation(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_FrshFlux_Precipitation(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_FrshFlux_Runoff(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_FrshFlux_SnowFall(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_HeatFlux_Latent(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- W/m2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_HeatFlux_LongWave(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- W/m2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_HeatFlux_Sensible(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- W/m2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_HeatFlux_ShortWave(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- W/m2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_HeatFlux_Total(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- W/m2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_stress_x(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- Pa
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_stress_xw(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- Pa
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_stress_y(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- Pa
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_stress_yw(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- Pa
- vgrid :
- surface
[34470887424 values with dtype=float32]
- cli(time, level_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- specific cloud ice content
- units :
- kg kg-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- clivi(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertically integrated cloud ice
- units :
- kg m-2
- vgrid :
- atmosphere
[34470887424 values with dtype=float32]
- cllvi(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertically integrated cloud water
- units :
- kg m-2
- vgrid :
- atmosphere
[34470887424 values with dtype=float32]
- clw(time, level_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- specific cloud water content
- units :
- kg kg-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- conc(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- ice concentration in each ice class
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- delhi(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Change in ice mean thickness due to thermodynamic effects
- units :
- m
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- dzghalf(level_full, cell)float32...
- long_name :
- vertical extent of model full layer
- standard_name :
- cell_thickness
- units :
- m
[283115520 values with dtype=float32]
- heatOceI(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Heat flux to ocean from the ice growth
- units :
- W/m^2
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- heatOceW(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Heat flux to ocean from the atmosphere
- units :
- W/m^2
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- heat_content_seaice(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- vgrid :
- surface
[34470887424 values with dtype=float32]
- heat_content_snow(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- vgrid :
- surface
[34470887424 values with dtype=float32]
- heat_content_total(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hfls(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- latent heat flux
- standard_name :
- surface_downward_latent_heat_flux
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hfss(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- sensible heat flux
- standard_name :
- surface_downward_sensible_heat_flux
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hi(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- ice thickness
- units :
- m
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- hs(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- snow thickness
- units :
- m
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- hus(time, level_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- Specific humidity
- standard_name :
- specific_humidity
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- hydro_canopy_cond_limited_box(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_discharge_ocean_box(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- units :
- m3 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_drainage_box(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- units :
- kg m-2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_ice_soil_sl_box(time, soil_depth_water_level, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- Ice content in soil layers
- units :
- m
- vgrid :
- soil_depth_water
[172354437120 values with dtype=float32]
- hydro_runoff_box(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- surface runoff
- units :
- kg m-2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_snow_soil_dens_box(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- Density of snow on soil
- units :
- kg m-3
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_transpiration_box(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- Transpiration from surface
- units :
- kg m-2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_weq_snow_box(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- Water content of snow reservoir on surface
- units :
- m
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_wtr_soil_sl_box(time, soil_depth_water_level, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- Water content in soil layers
- units :
- m
- vgrid :
- soil_depth_water
[172354437120 values with dtype=float32]
- ice_u(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- zonal velocity
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- ice_v(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- meridional velocity
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- mlotst(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- units :
- m
- vgrid :
- surface
[34470887424 values with dtype=float32]
- newice(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- new ice growth in open water
- units :
- m
- vgrid :
- surface
[34470887424 values with dtype=float32]
- ocean_fraction_depth_full(depth_full, cell)float32...
- long_name :
- fraction of cell covered by ocean
- standard_name :
- ocean_area_fraction
[226492416 values with dtype=float32]
- ocean_fraction_depth_half(depth_half, cell)float32...
- long_name :
- fraction of cell covered by ocean
- standard_name :
- ocean_area_fraction
[229638144 values with dtype=float32]
- ocean_fraction_surface(cell)float32...
- long_name :
- fraction of cell covered by ocean
- standard_name :
- ocean_area_fraction
[3145728 values with dtype=float32]
- pfull(time, level_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- standard_name :
- air_pressure
- units :
- Pa
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- phalf(time, level_half, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- standard_name :
- air_pressure
- units :
- Pa
- vgrid :
- reference_half
[3136850755584 values with dtype=float32]
- pr(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- precipitation flux
- units :
- kg m-2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- pres_msl(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- mean sea level pressure
- units :
- Pa
- vgrid :
- meansea
[34470887424 values with dtype=float32]
- pres_sfc(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- surface pressure
- standard_name :
- surface_air_pressure
- units :
- Pa
- vgrid :
- surface
[34470887424 values with dtype=float32]
- prls(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- vgrid :
- surface
[34470887424 values with dtype=float32]
- prw(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertically integrated water vapour
- units :
- kg m-2
- vgrid :
- atmosphere
[34470887424 values with dtype=float32]
- qg(time, level_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- specific graupel content
- units :
- kg kg-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- qgvi(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertically integrated graupel
- units :
- kg m-2
- vgrid :
- atmosphere
[34470887424 values with dtype=float32]
- qr(time, level_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- specific rain content
- units :
- kg kg-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- qrvi(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertically integrated rain
- units :
- kg m-2
- vgrid :
- atmosphere
[34470887424 values with dtype=float32]
- qs(time, level_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- specific snow content
- units :
- kg kg-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- qsvi(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertically integrated snow
- units :
- kg m-2
- vgrid :
- atmosphere
[34470887424 values with dtype=float32]
- qv2m(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- specific humidity in 2m
- units :
- kg kg-1
- vgrid :
- height_2m
[34470887424 values with dtype=float32]
- rlds(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- surface downwelling longwave radiation
- standard_name :
- surface_downwelling_longwave_flux_in_air
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- rlus(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- surface upwelling longwave radiation
- standard_name :
- surface_upwelling_longwave_flux_in_air
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- rlut(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- toa outgoing longwave radiation
- standard_name :
- toa_outgoing_longwave_flux
- units :
- W m-2
- vgrid :
- toa
[34470887424 values with dtype=float32]
- rsds(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- surface downwelling shortwave radiation
- standard_name :
- surface_downwelling_shortwave_flux_in_air
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- rsdt(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- toa incident shortwave radiation
- standard_name :
- toa_incoming_shortwave_flux
- units :
- W m-2
- vgrid :
- toa
[34470887424 values with dtype=float32]
- rsus(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- surface upwelling shortwave radiation
- standard_name :
- surface_upwelling_shortwave_flux_in_air
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- rsut(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- toa outgoing shortwave radiation
- standard_name :
- toa_outgoing_shortwave_flux
- units :
- W m-2
- vgrid :
- toa
[34470887424 values with dtype=float32]
- seb_forc_hflx_box(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- additional heat flux at surface
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- seb_heat_cap_box(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- surface layer heat capacity
- units :
- J m-2 K-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- sfcwind(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- 10m windspeed
- units :
- m s-1
- vgrid :
- height_10m
[34470887424 values with dtype=float32]
- sic(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- fraction of ocean covered by sea ice
- vgrid :
- surface
[34470887424 values with dtype=float32]
- sit(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- sea ice thickness
- units :
- m
- vgrid :
- surface
[34470887424 values with dtype=float32]
- snow_to_ice(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- amount of snow that is transformed to ice
- units :
- m
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- so(time, depth_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- sea water salinity
- standard_name :
- sea_water_salinity
- units :
- psu
- vgrid :
- depth_below_sea
[2481903894528 values with dtype=float32]
- sse_grnd_hflx_old_box(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- Ground heat flux (old)
- units :
- J m-2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- sse_t_soil_sl_box(time, soil_depth_energy_level, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- standard_name :
- soil_temperature
- units :
- K
- vgrid :
- soil_depth_energy
[172354437120 values with dtype=float32]
- stretch_c(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- zstar surface stretch at cell center
- units :
- m
- vgrid :
- surface
[34470887424 values with dtype=float32]
- ta(time, level_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- Temperature
- standard_name :
- air_temperature
- units :
- K
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- tas(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- temperature in 2m
- standard_name :
- air_temperature
- units :
- K
- vgrid :
- height_2m
[34470887424 values with dtype=float32]
- tauu(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- u-momentum flux at the surface
- units :
- N m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- tauv(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- v-momentum flux at the surface
- units :
- N m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- tke(time, depth_half, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- turbulent kinetic energy
- units :
- m2 s-2
- vgrid :
- depth_below_sea_half
[2516374781952 values with dtype=float32]
- to(time, depth_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- sea water potential temperature
- standard_name :
- sea_water_potential_temperature
- units :
- degC
- vgrid :
- depth_below_sea
[2481903894528 values with dtype=float32]
- ts(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- surface temperature
- standard_name :
- surface_temperature
- units :
- K
- vgrid :
- surface
[34470887424 values with dtype=float32]
- u(time, depth_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- u zonal velocity component
- units :
- m/s
- vgrid :
- depth_below_sea
[2481903894528 values with dtype=float32]
- u_vint(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- vertically integrated barotropic zonal velocity
- units :
- m2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- ua(time, level_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- Zonal wind
- standard_name :
- eastward_wind
- units :
- m s-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- uas(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- zonal wind in 10m
- units :
- m s-1
- vgrid :
- height_10m
[34470887424 values with dtype=float32]
- v(time, depth_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- v meridional velocity component
- units :
- m/s
- vgrid :
- depth_below_sea
[2481903894528 values with dtype=float32]
- v_vint(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- vertically integrated meridional zonal velocity
- units :
- m2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- va(time, level_full, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- Meridional wind
- standard_name :
- northward_wind
- units :
- m s-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- vas(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- meridional wind in 10m
- units :
- m s-1
- vgrid :
- height_10m
[34470887424 values with dtype=float32]
- w(time, depth_half, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- vertical velocity at cells
- units :
- m/s
- vgrid :
- depth_below_sea_half
[2516374781952 values with dtype=float32]
- wa_phy(time, level_half, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertical velocity
- units :
- m s-1
- vgrid :
- reference_half
[3136850755584 values with dtype=float32]
- zg(level_full, cell)float32...
- long_name :
- geometric height at full level center
- standard_name :
- height
- units :
- m
[283115520 values with dtype=float32]
- zghalf(level_half, cell)float32...
- long_name :
- geometric height at half level center
- standard_name :
- height
- units :
- m
[286261248 values with dtype=float32]
- zos(time, cell)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- zstar sfc elevation at cell center
- units :
- m
- vgrid :
- surface
[34470887424 values with dtype=float32]
- crsPandasIndex
PandasIndex(Index([nan], dtype='float32', name='crs'))
- depth_fullPandasIndex
PandasIndex(Index([ 1.0, 3.0999999046325684, 5.449999809265137, 8.100000381469727, 11.050000190734863, 14.350000381469727, 18.049999237060547, 22.200000762939453, 26.850000381469727, 32.0, 37.650001525878906, 43.79999923706055, 50.54999923706055, 57.95000076293945, 66.0, 74.80000305175781, 84.44999694824219, 95.0, 106.5, 119.0999984741211, 132.89999389648438, 147.9499969482422, 164.35000610351562, 182.25, 201.8000030517578, 223.14999389648438, 246.4499969482422, 271.8500061035156, 299.54998779296875, 329.75, 362.6499938964844, 398.45001220703125, 437.3999938964844, 479.25, 523.4500122070312, 569.5, 617.0999755859375, 666.2999877929688, 717.1500244140625, 769.7000122070312, 824.0, 880.0999755859375, 938.0499877929688, 997.9000244140625, 1059.75, 1124.5, 1194.0999755859375, 1270.699951171875, 1356.300048828125, 1451.699951171875, 1556.800048828125, 1671.949951171875, 1796.449951171875, 1929.5, 2071.39990234375, 2222.449951171875, 2382.89990234375, 2552.89990234375, 2732.5, 2921.75, 3120.60009765625, 3328.85009765625, 3546.25, 3772.449951171875, 4006.949951171875, 4249.14990234375, 4498.2998046875, 4753.5498046875, 5014.0, 5278.60009765625, 5546.25, 5815.75], dtype='float32', name='depth_full')) - depth_halfPandasIndex
PandasIndex(Index([ 0.0, 2.0, 4.199999809265137, 6.699999809265137, 9.5, 12.600000381469727, 16.100000381469727, 20.0, 24.399999618530273, 29.299999237060547, 34.70000076293945, 40.599998474121094, 47.0, 54.099998474121094, 61.79999923706055, 70.19999694824219, 79.4000015258789, 89.5, 100.5, 112.5, 125.69999694824219, 140.10000610351562, 155.8000030517578, 172.89999389648438, 191.60000610351562, 212.0, 234.3000030517578, 258.6000061035156, 285.1000061035156, 314.0, 345.5, 379.79998779296875, 417.1000061035156, 457.70001220703125, 500.79998779296875, 546.0999755859375, 592.9000244140625, 641.2999877929688, 691.2999877929688, 743.0, 796.4000244140625, 851.5999755859375, 908.5999755859375, 967.5, 1028.300048828125, 1091.199951171875, 1157.800048828125, 1230.4000244140625, 1311.0, 1401.5999755859375, 1501.800048828125, 1611.800048828125, 1732.0999755859375, 1860.800048828125, 1998.199951171875, 2144.60009765625, 2300.300048828125, 2465.5, 2640.300048828125, 2824.699951171875, 3018.800048828125, 3222.39990234375, 3435.300048828125, 3657.199951171875, 3887.699951171875, 4126.2001953125, 4372.10009765625, 4624.5, 4882.60009765625, 5145.39990234375, 5411.7998046875, 5680.7001953125, 5950.7998046875], dtype='float32', name='depth_half')) - level_fullPandasIndex
PandasIndex(Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90], dtype='int32', name='level_full')) - level_halfPandasIndex
PandasIndex(Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91], dtype='int32', name='level_half')) - soil_depth_energy_levelPandasIndex
PandasIndex(Index([0.032499998807907104, 0.19200000166893005, 0.7754999995231628, 2.683000087738037, 6.984000205993652], dtype='float32', name='soil_depth_energy_level')) - soil_depth_water_levelPandasIndex
PandasIndex(Index([0.032499998807907104, 0.19200000166893005, 0.7754999995231628, 2.683000087738037, 6.984000205993652], dtype='float32', name='soil_depth_water_level')) - timePandasIndex
PandasIndex(DatetimeIndex(['2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05', '2020-01-06', '2020-01-07', '2020-01-08', '2020-01-09', '2020-01-10', '2020-01-11', ... '2049-12-23', '2049-12-24', '2049-12-25', '2049-12-26', '2049-12-27', '2049-12-28', '2049-12-29', '2049-12-30', '2049-12-31', '2050-01-01'], dtype='datetime64[ns]', name='time', length=10958, freq=None))
Create UXarray Datasets from HEALPix
We can use from_healpix as follows to open a HEALPix grid from xarray.Dataset:
uxds = ux.UxDataset.from_healpix(ds)
uxds
<xarray.UxDataset> Size: 232TB
Dimensions: (time: 10958, depth_half: 73,
n_face: 3145728, level_full: 90,
crs: 1, depth_full: 72,
soil_depth_water_level: 5,
level_half: 91,
soil_depth_energy_level: 5)
Coordinates:
* crs (crs) float32 4B nan
* depth_full (depth_full) float32 288B 1.0 ... 5....
* depth_half (depth_half) float32 292B 0.0 ... 5....
* level_full (level_full) int32 360B 1 2 3 ... 89 90
* level_half (level_half) int32 364B 1 2 3 ... 90 91
* soil_depth_energy_level (soil_depth_energy_level) float32 20B ...
* soil_depth_water_level (soil_depth_water_level) float32 20B ...
* time (time) datetime64[ns] 88kB 2020-01-0...
Dimensions without coordinates: n_face
Data variables: (12/103)
A_tracer_v_to (time, depth_half, n_face) float32 10TB ...
FrshFlux_IceSalt (time, n_face) float32 138GB ...
FrshFlux_TotalIce (time, n_face) float32 138GB ...
Qbot (time, n_face) float32 138GB ...
Qtop (time, n_face) float32 138GB ...
Wind_Speed_10m (time, n_face) float32 138GB ...
... ...
vas (time, n_face) float32 138GB ...
w (time, depth_half, n_face) float32 10TB ...
wa_phy (time, level_half, n_face) float32 13TB ...
zg (level_full, n_face) float32 1GB ...
zghalf (level_half, n_face) float32 1GB ...
zos (time, n_face) float32 138GB ...<xarray.UxDataset> Size: 232TB
Dimensions: (time: 10958, depth_half: 73,
n_face: 3145728, level_full: 90,
crs: 1, depth_full: 72,
soil_depth_water_level: 5,
level_half: 91,
soil_depth_energy_level: 5)
Coordinates:
* crs (crs) float32 4B nan
* depth_full (depth_full) float32 288B 1.0 ... 5....
* depth_half (depth_half) float32 292B 0.0 ... 5....
* level_full (level_full) int32 360B 1 2 3 ... 89 90
* level_half (level_half) int32 364B 1 2 3 ... 90 91
* soil_depth_energy_level (soil_depth_energy_level) float32 20B ...
* soil_depth_water_level (soil_depth_water_level) float32 20B ...
* time (time) datetime64[ns] 88kB 2020-01-0...
Dimensions without coordinates: n_face
Data variables: (12/103)
A_tracer_v_to (time, depth_half, n_face) float32 10TB ...
FrshFlux_IceSalt (time, n_face) float32 138GB ...
FrshFlux_TotalIce (time, n_face) float32 138GB ...
Qbot (time, n_face) float32 138GB ...
Qtop (time, n_face) float32 138GB ...
Wind_Speed_10m (time, n_face) float32 138GB ...
... ...
vas (time, n_face) float32 138GB ...
w (time, depth_half, n_face) float32 10TB ...
wa_phy (time, level_half, n_face) float32 13TB ...
zg (level_full, n_face) float32 1GB ...
zghalf (level_half, n_face) float32 1GB ...
zos (time, n_face) float32 138GB ...- time: 10958
- depth_half: 73
- n_face: 3145728
- level_full: 90
- crs: 1
- depth_full: 72
- soil_depth_water_level: 5
- level_half: 91
- soil_depth_energy_level: 5
- crs(crs)float32nan
- grid_mapping_name :
- healpix
- healpix_nside :
- 512
- healpix_order :
- nest
array([nan], dtype=float32)
- depth_full(depth_full)float321.0 3.1 ... 5.546e+03 5.816e+03
- axis :
- Z
- long_name :
- depth_below_sea
- positive :
- down
- standard_name :
- depth
- units :
- m
array([1.00000e+00, 3.10000e+00, 5.45000e+00, 8.10000e+00, 1.10500e+01, 1.43500e+01, 1.80500e+01, 2.22000e+01, 2.68500e+01, 3.20000e+01, 3.76500e+01, 4.38000e+01, 5.05500e+01, 5.79500e+01, 6.60000e+01, 7.48000e+01, 8.44500e+01, 9.50000e+01, 1.06500e+02, 1.19100e+02, 1.32900e+02, 1.47950e+02, 1.64350e+02, 1.82250e+02, 2.01800e+02, 2.23150e+02, 2.46450e+02, 2.71850e+02, 2.99550e+02, 3.29750e+02, 3.62650e+02, 3.98450e+02, 4.37400e+02, 4.79250e+02, 5.23450e+02, 5.69500e+02, 6.17100e+02, 6.66300e+02, 7.17150e+02, 7.69700e+02, 8.24000e+02, 8.80100e+02, 9.38050e+02, 9.97900e+02, 1.05975e+03, 1.12450e+03, 1.19410e+03, 1.27070e+03, 1.35630e+03, 1.45170e+03, 1.55680e+03, 1.67195e+03, 1.79645e+03, 1.92950e+03, 2.07140e+03, 2.22245e+03, 2.38290e+03, 2.55290e+03, 2.73250e+03, 2.92175e+03, 3.12060e+03, 3.32885e+03, 3.54625e+03, 3.77245e+03, 4.00695e+03, 4.24915e+03, 4.49830e+03, 4.75355e+03, 5.01400e+03, 5.27860e+03, 5.54625e+03, 5.81575e+03], dtype=float32) - depth_half(depth_half)float320.0 2.0 4.2 ... 5.681e+03 5.951e+03
- axis :
- Z
- long_name :
- depth_below_sea
- positive :
- down
- standard_name :
- depth
- units :
- m
array([0.0000e+00, 2.0000e+00, 4.2000e+00, 6.7000e+00, 9.5000e+00, 1.2600e+01, 1.6100e+01, 2.0000e+01, 2.4400e+01, 2.9300e+01, 3.4700e+01, 4.0600e+01, 4.7000e+01, 5.4100e+01, 6.1800e+01, 7.0200e+01, 7.9400e+01, 8.9500e+01, 1.0050e+02, 1.1250e+02, 1.2570e+02, 1.4010e+02, 1.5580e+02, 1.7290e+02, 1.9160e+02, 2.1200e+02, 2.3430e+02, 2.5860e+02, 2.8510e+02, 3.1400e+02, 3.4550e+02, 3.7980e+02, 4.1710e+02, 4.5770e+02, 5.0080e+02, 5.4610e+02, 5.9290e+02, 6.4130e+02, 6.9130e+02, 7.4300e+02, 7.9640e+02, 8.5160e+02, 9.0860e+02, 9.6750e+02, 1.0283e+03, 1.0912e+03, 1.1578e+03, 1.2304e+03, 1.3110e+03, 1.4016e+03, 1.5018e+03, 1.6118e+03, 1.7321e+03, 1.8608e+03, 1.9982e+03, 2.1446e+03, 2.3003e+03, 2.4655e+03, 2.6403e+03, 2.8247e+03, 3.0188e+03, 3.2224e+03, 3.4353e+03, 3.6572e+03, 3.8877e+03, 4.1262e+03, 4.3721e+03, 4.6245e+03, 4.8826e+03, 5.1454e+03, 5.4118e+03, 5.6807e+03, 5.9508e+03], dtype=float32) - level_full(level_full)int321 2 3 4 5 6 7 ... 85 86 87 88 89 90
- axis :
- Z
- long_name :
- generalized_height
- positive :
- down
- standard_name :
- height
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90], dtype=int32) - level_half(level_half)int321 2 3 4 5 6 7 ... 86 87 88 89 90 91
- axis :
- Z
- long_name :
- generalized_height
- positive :
- down
- standard_name :
- height
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91], dtype=int32) - soil_depth_energy_level(soil_depth_energy_level)float320.0325 0.192 0.7755 2.683 6.984
- axis :
- Z
- long_name :
- depth_below_land
- positive :
- down
- units :
- m
array([0.0325, 0.192 , 0.7755, 2.683 , 6.984 ], dtype=float32)
- soil_depth_water_level(soil_depth_water_level)float320.0325 0.192 0.7755 2.683 6.984
- axis :
- Z
- long_name :
- depth_below_land
- positive :
- down
- units :
- m
array([0.0325, 0.192 , 0.7755, 2.683 , 6.984 ], dtype=float32)
- time(time)datetime64[ns]2020-01-02 ... 2050-01-01
- axis :
- T
array(['2020-01-02T00:00:00.000000000', '2020-01-03T00:00:00.000000000', '2020-01-04T00:00:00.000000000', ..., '2049-12-30T00:00:00.000000000', '2049-12-31T00:00:00.000000000', '2050-01-01T00:00:00.000000000'], shape=(10958,), dtype='datetime64[ns]')
- A_tracer_v_to(time, depth_half, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- sea water vertical diffusivity
- standard_name :
- ocean_vertical_diffusivity
- units :
- m2 s-1
- vgrid :
- depth_below_sea_half
[2516374781952 values with dtype=float32]
- FrshFlux_IceSalt(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Salt volume flux due to sea ice change
- units :
- psu m s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- FrshFlux_TotalIce(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Freshwater Flux due to Sea Ice Change
- units :
- m s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- Qbot(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Conductive heat flux at ice-ocean interface
- units :
- W/m^2
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- Qtop(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Energy flux available for surface melting
- units :
- W/m^2
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- Wind_Speed_10m(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Wind Speed at 10m height
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_FrshFlux_Evaporation(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_FrshFlux_Precipitation(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_FrshFlux_Runoff(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_FrshFlux_SnowFall(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_HeatFlux_Latent(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- W/m2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_HeatFlux_LongWave(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- W/m2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_HeatFlux_Sensible(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- W/m2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_HeatFlux_ShortWave(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- W/m2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_HeatFlux_Total(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- W/m2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_stress_x(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- Pa
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_stress_xw(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- Pa
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_stress_y(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- Pa
- vgrid :
- surface
[34470887424 values with dtype=float32]
- atmos_fluxes_stress_yw(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- units :
- Pa
- vgrid :
- surface
[34470887424 values with dtype=float32]
- cli(time, level_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- specific cloud ice content
- units :
- kg kg-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- clivi(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertically integrated cloud ice
- units :
- kg m-2
- vgrid :
- atmosphere
[34470887424 values with dtype=float32]
- cllvi(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertically integrated cloud water
- units :
- kg m-2
- vgrid :
- atmosphere
[34470887424 values with dtype=float32]
- clw(time, level_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- specific cloud water content
- units :
- kg kg-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- conc(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- ice concentration in each ice class
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- delhi(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Change in ice mean thickness due to thermodynamic effects
- units :
- m
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- dzghalf(level_full, n_face)float32...
- long_name :
- vertical extent of model full layer
- standard_name :
- cell_thickness
- units :
- m
[283115520 values with dtype=float32]
- heatOceI(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Heat flux to ocean from the ice growth
- units :
- W/m^2
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- heatOceW(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- Heat flux to ocean from the atmosphere
- units :
- W/m^2
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- heat_content_seaice(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- vgrid :
- surface
[34470887424 values with dtype=float32]
- heat_content_snow(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- vgrid :
- surface
[34470887424 values with dtype=float32]
- heat_content_total(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hfls(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- latent heat flux
- standard_name :
- surface_downward_latent_heat_flux
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hfss(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- sensible heat flux
- standard_name :
- surface_downward_sensible_heat_flux
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hi(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- ice thickness
- units :
- m
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- hs(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- snow thickness
- units :
- m
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- hus(time, level_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- Specific humidity
- standard_name :
- specific_humidity
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- hydro_canopy_cond_limited_box(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_discharge_ocean_box(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- units :
- m3 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_drainage_box(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- units :
- kg m-2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_ice_soil_sl_box(time, soil_depth_water_level, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- Ice content in soil layers
- units :
- m
- vgrid :
- soil_depth_water
[172354437120 values with dtype=float32]
- hydro_runoff_box(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- surface runoff
- units :
- kg m-2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_snow_soil_dens_box(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- Density of snow on soil
- units :
- kg m-3
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_transpiration_box(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- Transpiration from surface
- units :
- kg m-2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_weq_snow_box(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- Water content of snow reservoir on surface
- units :
- m
- vgrid :
- surface
[34470887424 values with dtype=float32]
- hydro_wtr_soil_sl_box(time, soil_depth_water_level, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- Water content in soil layers
- units :
- m
- vgrid :
- soil_depth_water
[172354437120 values with dtype=float32]
- ice_u(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- zonal velocity
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- ice_v(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- meridional velocity
- units :
- m/s
- vgrid :
- surface
[34470887424 values with dtype=float32]
- mlotst(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- standard_name :
- ocean_mixed_layer_thickness_defined_by_sigma_t
- units :
- m
- vgrid :
- surface
[34470887424 values with dtype=float32]
- newice(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- new ice growth in open water
- units :
- m
- vgrid :
- surface
[34470887424 values with dtype=float32]
- ocean_fraction_depth_full(depth_full, n_face)float32...
- long_name :
- fraction of cell covered by ocean
- standard_name :
- ocean_area_fraction
[226492416 values with dtype=float32]
- ocean_fraction_depth_half(depth_half, n_face)float32...
- long_name :
- fraction of cell covered by ocean
- standard_name :
- ocean_area_fraction
[229638144 values with dtype=float32]
- ocean_fraction_surface(n_face)float32...
- long_name :
- fraction of cell covered by ocean
- standard_name :
- ocean_area_fraction
[3145728 values with dtype=float32]
- pfull(time, level_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- standard_name :
- air_pressure
- units :
- Pa
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- phalf(time, level_half, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- standard_name :
- air_pressure
- units :
- Pa
- vgrid :
- reference_half
[3136850755584 values with dtype=float32]
- pr(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- precipitation flux
- units :
- kg m-2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- pres_msl(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- mean sea level pressure
- units :
- Pa
- vgrid :
- meansea
[34470887424 values with dtype=float32]
- pres_sfc(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- surface pressure
- standard_name :
- surface_air_pressure
- units :
- Pa
- vgrid :
- surface
[34470887424 values with dtype=float32]
- prls(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- vgrid :
- surface
[34470887424 values with dtype=float32]
- prw(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertically integrated water vapour
- units :
- kg m-2
- vgrid :
- atmosphere
[34470887424 values with dtype=float32]
- qg(time, level_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- specific graupel content
- units :
- kg kg-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- qgvi(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertically integrated graupel
- units :
- kg m-2
- vgrid :
- atmosphere
[34470887424 values with dtype=float32]
- qr(time, level_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- specific rain content
- units :
- kg kg-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- qrvi(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertically integrated rain
- units :
- kg m-2
- vgrid :
- atmosphere
[34470887424 values with dtype=float32]
- qs(time, level_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- specific snow content
- units :
- kg kg-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- qsvi(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertically integrated snow
- units :
- kg m-2
- vgrid :
- atmosphere
[34470887424 values with dtype=float32]
- qv2m(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- specific humidity in 2m
- units :
- kg kg-1
- vgrid :
- height_2m
[34470887424 values with dtype=float32]
- rlds(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- surface downwelling longwave radiation
- standard_name :
- surface_downwelling_longwave_flux_in_air
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- rlus(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- surface upwelling longwave radiation
- standard_name :
- surface_upwelling_longwave_flux_in_air
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- rlut(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- toa outgoing longwave radiation
- standard_name :
- toa_outgoing_longwave_flux
- units :
- W m-2
- vgrid :
- toa
[34470887424 values with dtype=float32]
- rsds(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- surface downwelling shortwave radiation
- standard_name :
- surface_downwelling_shortwave_flux_in_air
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- rsdt(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- toa incident shortwave radiation
- standard_name :
- toa_incoming_shortwave_flux
- units :
- W m-2
- vgrid :
- toa
[34470887424 values with dtype=float32]
- rsus(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- surface upwelling shortwave radiation
- standard_name :
- surface_upwelling_shortwave_flux_in_air
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- rsut(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- toa outgoing shortwave radiation
- standard_name :
- toa_outgoing_shortwave_flux
- units :
- W m-2
- vgrid :
- toa
[34470887424 values with dtype=float32]
- seb_forc_hflx_box(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- additional heat flux at surface
- units :
- W m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- seb_heat_cap_box(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- surface layer heat capacity
- units :
- J m-2 K-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- sfcwind(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- 10m windspeed
- units :
- m s-1
- vgrid :
- height_10m
[34470887424 values with dtype=float32]
- sic(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- fraction of ocean covered by sea ice
- vgrid :
- surface
[34470887424 values with dtype=float32]
- sit(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- sea ice thickness
- units :
- m
- vgrid :
- surface
[34470887424 values with dtype=float32]
- snow_to_ice(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- amount of snow that is transformed to ice
- units :
- m
- vgrid :
- generic_ice
[34470887424 values with dtype=float32]
- so(time, depth_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- sea water salinity
- standard_name :
- sea_water_salinity
- units :
- psu
- vgrid :
- depth_below_sea
[2481903894528 values with dtype=float32]
- sse_grnd_hflx_old_box(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- long_name :
- Ground heat flux (old)
- units :
- J m-2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- sse_t_soil_sl_box(time, soil_depth_energy_level, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- jsbach
- grid_mapping :
- crs
- standard_name :
- soil_temperature
- units :
- K
- vgrid :
- soil_depth_energy
[172354437120 values with dtype=float32]
- stretch_c(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- zstar surface stretch at cell center
- units :
- m
- vgrid :
- surface
[34470887424 values with dtype=float32]
- ta(time, level_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- Temperature
- standard_name :
- air_temperature
- units :
- K
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- tas(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- temperature in 2m
- standard_name :
- air_temperature
- units :
- K
- vgrid :
- height_2m
[34470887424 values with dtype=float32]
- tauu(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- u-momentum flux at the surface
- units :
- N m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- tauv(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- v-momentum flux at the surface
- units :
- N m-2
- vgrid :
- surface
[34470887424 values with dtype=float32]
- tke(time, depth_half, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- turbulent kinetic energy
- units :
- m2 s-2
- vgrid :
- depth_below_sea_half
[2516374781952 values with dtype=float32]
- to(time, depth_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- sea water potential temperature
- standard_name :
- sea_water_potential_temperature
- units :
- degC
- vgrid :
- depth_below_sea
[2481903894528 values with dtype=float32]
- ts(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- surface temperature
- standard_name :
- surface_temperature
- units :
- K
- vgrid :
- surface
[34470887424 values with dtype=float32]
- u(time, depth_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- u zonal velocity component
- units :
- m/s
- vgrid :
- depth_below_sea
[2481903894528 values with dtype=float32]
- u_vint(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- vertically integrated barotropic zonal velocity
- units :
- m2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- ua(time, level_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- Zonal wind
- standard_name :
- eastward_wind
- units :
- m s-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- uas(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- zonal wind in 10m
- units :
- m s-1
- vgrid :
- height_10m
[34470887424 values with dtype=float32]
- v(time, depth_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- v meridional velocity component
- units :
- m/s
- vgrid :
- depth_below_sea
[2481903894528 values with dtype=float32]
- v_vint(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- vertically integrated meridional zonal velocity
- units :
- m2 s-1
- vgrid :
- surface
[34470887424 values with dtype=float32]
- va(time, level_full, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- Meridional wind
- standard_name :
- northward_wind
- units :
- m s-1
- vgrid :
- reference
[3102379868160 values with dtype=float32]
- vas(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- meridional wind in 10m
- units :
- m s-1
- vgrid :
- height_10m
[34470887424 values with dtype=float32]
- w(time, depth_half, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- vertical velocity at cells
- units :
- m/s
- vgrid :
- depth_below_sea_half
[2516374781952 values with dtype=float32]
- wa_phy(time, level_half, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- vertical velocity
- units :
- m s-1
- vgrid :
- reference_half
[3136850755584 values with dtype=float32]
- zg(level_full, n_face)float32...
- long_name :
- geometric height at full level center
- standard_name :
- height
- units :
- m
[283115520 values with dtype=float32]
- zghalf(level_half, n_face)float32...
- long_name :
- geometric height at half level center
- standard_name :
- height
- units :
- m
[286261248 values with dtype=float32]
- zos(time, n_face)float32...
- cell_methods :
- time: mean cell: mean
- component :
- ocean
- grid_mapping :
- crs
- long_name :
- zstar sfc elevation at cell center
- units :
- m
- vgrid :
- surface
[34470887424 values with dtype=float32]
- crsPandasIndex
PandasIndex(Index([nan], dtype='float32', name='crs'))
- depth_fullPandasIndex
PandasIndex(Index([ 1.0, 3.0999999046325684, 5.449999809265137, 8.100000381469727, 11.050000190734863, 14.350000381469727, 18.049999237060547, 22.200000762939453, 26.850000381469727, 32.0, 37.650001525878906, 43.79999923706055, 50.54999923706055, 57.95000076293945, 66.0, 74.80000305175781, 84.44999694824219, 95.0, 106.5, 119.0999984741211, 132.89999389648438, 147.9499969482422, 164.35000610351562, 182.25, 201.8000030517578, 223.14999389648438, 246.4499969482422, 271.8500061035156, 299.54998779296875, 329.75, 362.6499938964844, 398.45001220703125, 437.3999938964844, 479.25, 523.4500122070312, 569.5, 617.0999755859375, 666.2999877929688, 717.1500244140625, 769.7000122070312, 824.0, 880.0999755859375, 938.0499877929688, 997.9000244140625, 1059.75, 1124.5, 1194.0999755859375, 1270.699951171875, 1356.300048828125, 1451.699951171875, 1556.800048828125, 1671.949951171875, 1796.449951171875, 1929.5, 2071.39990234375, 2222.449951171875, 2382.89990234375, 2552.89990234375, 2732.5, 2921.75, 3120.60009765625, 3328.85009765625, 3546.25, 3772.449951171875, 4006.949951171875, 4249.14990234375, 4498.2998046875, 4753.5498046875, 5014.0, 5278.60009765625, 5546.25, 5815.75], dtype='float32', name='depth_full')) - depth_halfPandasIndex
PandasIndex(Index([ 0.0, 2.0, 4.199999809265137, 6.699999809265137, 9.5, 12.600000381469727, 16.100000381469727, 20.0, 24.399999618530273, 29.299999237060547, 34.70000076293945, 40.599998474121094, 47.0, 54.099998474121094, 61.79999923706055, 70.19999694824219, 79.4000015258789, 89.5, 100.5, 112.5, 125.69999694824219, 140.10000610351562, 155.8000030517578, 172.89999389648438, 191.60000610351562, 212.0, 234.3000030517578, 258.6000061035156, 285.1000061035156, 314.0, 345.5, 379.79998779296875, 417.1000061035156, 457.70001220703125, 500.79998779296875, 546.0999755859375, 592.9000244140625, 641.2999877929688, 691.2999877929688, 743.0, 796.4000244140625, 851.5999755859375, 908.5999755859375, 967.5, 1028.300048828125, 1091.199951171875, 1157.800048828125, 1230.4000244140625, 1311.0, 1401.5999755859375, 1501.800048828125, 1611.800048828125, 1732.0999755859375, 1860.800048828125, 1998.199951171875, 2144.60009765625, 2300.300048828125, 2465.5, 2640.300048828125, 2824.699951171875, 3018.800048828125, 3222.39990234375, 3435.300048828125, 3657.199951171875, 3887.699951171875, 4126.2001953125, 4372.10009765625, 4624.5, 4882.60009765625, 5145.39990234375, 5411.7998046875, 5680.7001953125, 5950.7998046875], dtype='float32', name='depth_half')) - level_fullPandasIndex
PandasIndex(Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90], dtype='int32', name='level_full')) - level_halfPandasIndex
PandasIndex(Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91], dtype='int32', name='level_half')) - soil_depth_energy_levelPandasIndex
PandasIndex(Index([0.032499998807907104, 0.19200000166893005, 0.7754999995231628, 2.683000087738037, 6.984000205993652], dtype='float32', name='soil_depth_energy_level')) - soil_depth_water_levelPandasIndex
PandasIndex(Index([0.032499998807907104, 0.19200000166893005, 0.7754999995231628, 2.683000087738037, 6.984000205993652], dtype='float32', name='soil_depth_water_level')) - timePandasIndex
PandasIndex(DatetimeIndex(['2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05', '2020-01-06', '2020-01-07', '2020-01-08', '2020-01-09', '2020-01-10', '2020-01-11', ... '2049-12-23', '2049-12-24', '2049-12-25', '2049-12-26', '2049-12-27', '2049-12-28', '2049-12-29', '2049-12-30', '2049-12-31', '2050-01-01'], dtype='datetime64[ns]', name='time', length=10958, freq=None))
Show Grid Information
<uxarray.Grid> Original Grid Type: HEALPix Grid Dimensions: * n_face: 3145728 Grid Coordinates (Spherical): * face_lon: (3145728,) * face_lat: (3145728,) Grid Coordinates (Cartesian): Grid Connectivity Variables: Grid Descriptor Variables:
- n_face: 3145728
- face_lon(n_face)float6445.0 45.09 44.91 ... -45.09 -45.0
- standard_name :
- longitude
- long name :
- Longitude of the center of each face
- units :
- degrees_east
array([ 45. , 45.08789062, 44.91210938, ..., -44.91210938, -45.08789062, -45. ], shape=(3145728,)) - face_lat(n_face)float640.0746 0.1492 ... -0.1492 -0.0746
- standard_name :
- latitude
- long name :
- Latitude of the center of each face
- units :
- degrees_north
array([ 0.0746039 , 0.14920793, 0.14920793, ..., -0.14920793, -0.14920793, -0.0746039 ], shape=(3145728,))
- zoom :
- 9
- n_side :
- 512
- n_pix :
- 3145728
- nest :
- True
- source_grid_spec :
- HEALPix
uxds.uxgrid.face_node_connectivity
<xarray.DataArray 'face_node_connectivity' (n_face: 3145728, n_max_face_nodes: 4)> Size: 101MB
array([[ 778523, 1700461, 2064568, 1448407],
[ 261422, 2488188, 1700461, 778523],
[1700461, 1778639, 2171953, 2064568],
...,
[1147106, 293699, 791866, 409789],
[ 791866, 2110736, 1740665, 2548812],
[ 293699, 2286345, 2110736, 791866]], shape=(3145728, 4))
Dimensions without coordinates: n_face, n_max_face_nodes
Attributes:
cf_role: face_node_connectivity
long name: Maps every face to its corner nodes.
start_index: 0
_FillValue: -9223372036854775808- n_face: 3145728
- n_max_face_nodes: 4
- 778523 1700461 2064568 1448407 ... 293699 2286345 2110736 791866
array([[ 778523, 1700461, 2064568, 1448407], [ 261422, 2488188, 1700461, 778523], [1700461, 1778639, 2171953, 2064568], ..., [1147106, 293699, 791866, 409789], [ 791866, 2110736, 1740665, 2548812], [ 293699, 2286345, 2110736, 791866]], shape=(3145728, 4)) - cf_role :
- face_node_connectivity
- long name :
- Maps every face to its corner nodes.
- start_index :
- 0
- _FillValue :
- -9223372036854775808
Data variable of interest
Then let us pick a variable from the dataset, which will give us an uxarray.UxDataArray:
uxda = uxds['tas']
uxda
<xarray.UxDataArray 'tas' (time: 10958, n_face: 3145728)> Size: 138GB
[34470887424 values with dtype=float32]
Coordinates:
* time (time) datetime64[ns] 88kB 2020-01-02 2020-01-03 ... 2050-01-01
Dimensions without coordinates: n_face
Attributes:
cell_methods: time: mean cell: mean
component: atmo
grid_mapping: crs
long_name: temperature in 2m
standard_name: air_temperature
units: K
vgrid: height_2m<xarray.UxDataArray 'tas' (time: 10958, n_face: 3145728)> Size: 138GB
[34470887424 values with dtype=float32]
Coordinates:
* time (time) datetime64[ns] 88kB 2020-01-02 2020-01-03 ... 2050-01-01
Dimensions without coordinates: n_face
Attributes:
cell_methods: time: mean cell: mean
component: atmo
grid_mapping: crs
long_name: temperature in 2m
standard_name: air_temperature
units: K
vgrid: height_2m- time: 10958
- n_face: 3145728
- ...
[34470887424 values with dtype=float32]
- time(time)datetime64[ns]2020-01-02 ... 2050-01-01
- axis :
- T
array(['2020-01-02T00:00:00.000000000', '2020-01-03T00:00:00.000000000', '2020-01-04T00:00:00.000000000', ..., '2049-12-30T00:00:00.000000000', '2049-12-31T00:00:00.000000000', '2050-01-01T00:00:00.000000000'], shape=(10958,), dtype='datetime64[ns]')
- timePandasIndex
PandasIndex(DatetimeIndex(['2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05', '2020-01-06', '2020-01-07', '2020-01-08', '2020-01-09', '2020-01-10', '2020-01-11', ... '2049-12-23', '2049-12-24', '2049-12-25', '2049-12-26', '2049-12-27', '2049-12-28', '2049-12-29', '2049-12-30', '2049-12-31', '2050-01-01'], dtype='datetime64[ns]', name='time', length=10958, freq=None))
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- temperature in 2m
- standard_name :
- air_temperature
- units :
- K
- vgrid :
- height_2m
Show Grid Information
<uxarray.Grid> Original Grid Type: HEALPix Grid Dimensions: * n_node: 3145730 * n_face: 3145728 * n_max_face_nodes: 4 Grid Coordinates (Spherical): * node_lon: (3145730,) * node_lat: (3145730,) * face_lon: (3145728,) * face_lat: (3145728,) Grid Coordinates (Cartesian): Grid Connectivity Variables: * face_node_connectivity: (3145728, 4) Grid Descriptor Variables:
- n_face: 3145728
- n_node: 3145730
- n_max_face_nodes: 4
- node_lon(n_node)float6491.05 -102.6 -76.06 ... 139.0 112.3
- standard_name :
- longitude
- long name :
- Longitude of the corner nodes of each face
- units :
- degrees_east
array([ 91.0546875 , -102.56835938, -76.05633803, ..., 22.58789062, 139.01785714, 112.32421875], shape=(3145730,)) - node_lat(n_node)float64-23.64 -36.7 77.0 ... -69.42 14.63
- standard_name :
- latitude
- long name :
- Latitude of the corner nodes of each face
- units :
- degrees_north
array([-23.64331426, -36.70222269, 76.99747443, ..., -20.50324418, -69.42254649, 14.63166703], shape=(3145730,)) - face_lon(n_face)float6445.0 45.09 44.91 ... -45.09 -45.0
- standard_name :
- longitude
- long name :
- Longitude of the center of each face
- units :
- degrees_east
array([ 45. , 45.08789062, 44.91210938, ..., -44.91210938, -45.08789062, -45. ], shape=(3145728,)) - face_lat(n_face)float640.0746 0.1492 ... -0.1492 -0.0746
- standard_name :
- latitude
- long name :
- Latitude of the center of each face
- units :
- degrees_north
array([ 0.0746039 , 0.14920793, 0.14920793, ..., -0.14920793, -0.14920793, -0.0746039 ], shape=(3145728,))
- face_node_connectivity(n_face, n_max_face_nodes)int64778523 1700461 ... 2110736 791866
- cf_role :
- face_node_connectivity
- long name :
- Maps every face to its corner nodes.
- start_index :
- 0
- _FillValue :
- -9223372036854775808
array([[ 778523, 1700461, 2064568, 1448407], [ 261422, 2488188, 1700461, 778523], [1700461, 1778639, 2171953, 2064568], ..., [1147106, 293699, 791866, 409789], [ 791866, 2110736, 1740665, 2548812], [ 293699, 2286345, 2110736, 791866]], shape=(3145728, 4))
- zoom :
- 9
- n_side :
- 512
- n_pix :
- 3145728
- nest :
- True
- source_grid_spec :
- HEALPix
Global mean and plot
Computing the global air temperature mean (at the first timestep) and also having a quick plot of it would be a good idea to have as references to compare the upcoming analyses & visualizations to them:
%%time
print("Global air temperature average on ", uxda.time[0].values, ": ", uxda.isel(time=0).mean().values, " K")
Global air temperature average on 2020-01-02T00:00:00.000000000 : 286.3096 K
CPU times: user 490 ms, sys: 279 ms, total: 769 ms
Wall time: 1.61 s
%%time
projection = ccrs.Robinson()
uxda.isel(time=0).plot(
projection=projection,
cmap="inferno",
features=["borders", "coastline"],
title="Global temperature",
width=700,
)
CPU times: user 12.4 s, sys: 588 ms, total: 13 s
Wall time: 13.6 s
WARNING:param.GeoOverlayPlot00477: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
Cross-sections
We can look at constant latitude/longitude cross-sections of an uxarray.UxDataArray:
boulder_lat = 40.0190
# With fine resolutions like zoom level of 9, it is visually hard to observe the cross-sections,
# so we will use a zoom level of 4 for a better visualization
uxda_coarse = ux.UxDataset.from_healpix(model_run(zoom=4, time="P1D").to_dask())['tas']
uxda_coarse.uxgrid.face_node_connectivity
uxda_lat = uxda_coarse.cross_section.constant_latitude(boulder_lat)
uxda_lat
/home/runner/miniconda3/envs/healpix-cookbook-dev/lib/python3.10/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
'dims': dict(self._ds.dims),
<xarray.UxDataArray 'tas' (time: 10958, n_face: 128)> Size: 6MB
[1402624 values with dtype=float32]
Coordinates:
* time (time) datetime64[ns] 88kB 2020-01-02 2020-01-03 ... 2050-01-01
Dimensions without coordinates: n_face
Attributes:
cell_methods: time: mean cell: mean
component: atmo
grid_mapping: crs
long_name: temperature in 2m
standard_name: air_temperature
units: K
vgrid: height_2m<xarray.UxDataArray 'tas' (time: 10958, n_face: 128)> Size: 6MB
[1402624 values with dtype=float32]
Coordinates:
* time (time) datetime64[ns] 88kB 2020-01-02 2020-01-03 ... 2050-01-01
Dimensions without coordinates: n_face
Attributes:
cell_methods: time: mean cell: mean
component: atmo
grid_mapping: crs
long_name: temperature in 2m
standard_name: air_temperature
units: K
vgrid: height_2m- time: 10958
- n_face: 128
- ...
[1402624 values with dtype=float32]
- time(time)datetime64[ns]2020-01-02 ... 2050-01-01
- axis :
- T
array(['2020-01-02T00:00:00.000000000', '2020-01-03T00:00:00.000000000', '2020-01-04T00:00:00.000000000', ..., '2049-12-30T00:00:00.000000000', '2049-12-31T00:00:00.000000000', '2050-01-01T00:00:00.000000000'], shape=(10958,), dtype='datetime64[ns]')
- timePandasIndex
PandasIndex(DatetimeIndex(['2020-01-02', '2020-01-03', '2020-01-04', '2020-01-05', '2020-01-06', '2020-01-07', '2020-01-08', '2020-01-09', '2020-01-10', '2020-01-11', ... '2049-12-23', '2049-12-24', '2049-12-25', '2049-12-26', '2049-12-27', '2049-12-28', '2049-12-29', '2049-12-30', '2049-12-31', '2050-01-01'], dtype='datetime64[ns]', name='time', length=10958, freq=None))
- cell_methods :
- time: mean cell: mean
- component :
- atmo
- grid_mapping :
- crs
- long_name :
- temperature in 2m
- standard_name :
- air_temperature
- units :
- K
- vgrid :
- height_2m
Show Grid Information
<uxarray.Grid> Original Grid Type: HEALPix Grid Dimensions: * n_node: 252 * n_face: 128 * n_max_face_nodes: 4 Grid Coordinates (Spherical): * node_lon: (252,) * node_lat: (252,) * face_lon: (128,) * face_lat: (128,) Grid Coordinates (Cartesian): * node_x: (252,) * node_y: (252,) * node_z: (252,) Grid Connectivity Variables: * face_node_connectivity: (128, 4) Grid Descriptor Variables: * n_nodes_per_face: (128,)
- n_face: 128
- n_node: 252
- n_max_face_nodes: 4
- lon_lat: 2
- min_max: 2
- node_lon(n_node)float64114.0 -120.9 135.0 ... 101.2 -70.31
- standard_name :
- longitude
- long name :
- Longitude of the corner nodes of each face
- units :
- degrees_east
array([ 114. , -120.9375, 135. , 81.5625, -84.375 , -132. , -126.5625, -144. , 33.75 , -19.6875, 156. , 59.0625, 24. , -90. , -36.5625, 87.1875, 56.25 , 73.125 , 112.5 , -53.4375, 45. , 135. , -95.625 , 106.875 , 84.375 , -106.875 , 11.25 , -120. , -168.75 , 126. , -138. , -14.0625, -168.75 , 5.625 , 48. , -112.5 , -2.8125, 95.625 , -177.1875, -81.5625, 30. , -50.625 , 72. , -163.125 , -114. , 25.3125, 90. , 126.5625, 75.9375, 33.75 , 8.4375, -84.375 , -129.375 , 90. , -146.25 , 106.875 , -96. , 108. , 151.875 , 54. , -146.25 , 104.0625, -174. , 67.5 , -30.9375, 157.5 , -39.375 , -112.5 , 78.75 , 150. , 39.375 , -174.375 , 2.8125, -11.25 , 42.1875, 118.125 , -78.75 , 123.75 , 177.1875, 98.4375, 36.5625, -64.6875, -154.6875, -157.5 , -102. , -54. , -75.9375, 171.5625, 157.5 , 28.125 , -66. , -73.125 , 168.75 , -39.375 , 14.0625, -56.25 , -61.875 , 47.8125, -165.9375, -16.875 , -22.5 , 12. , 174.375 , -16.875 , -28.125 , 64.6875, 39.375 , 61.875 , -73.125 , 56.25 , 118.125 , 174.375 , 144. , -109.6875, 138. , -45. , -24. , 143.4375, 45. , -90. , ... 132. , 0. , -11.25 , 22.5 , -8.4375, -150. , 163.125 , -156. , 102. , 61.875 , -5.625 , 0. , 36. , 78.75 , -160.3125, -78. , 146.25 , 6. , 28.125 , 132.1875, -42. , -132.1875, -67.5 , 90. , 50.625 , -67.5 , 140.625 , 162. , -162. , -42.1875, 96. , -180. , -118.125 , -149.0625, -168. , 160.3125, -28.125 , -174.375 , 112.5 , -151.875 , -108. , -36. , -59.0625, 123.75 , -5.625 , -92.8125, 84.375 , -22.5 , 168.75 , -98.4375, 168. , -84. , 154.6875, 11.25 , -48. , 129.375 , -45. , 60. , -78.75 , -157.5 , -135. , -6. , 146.25 , 151.875 , -72. , 30.9375, 115.3125, -115.3125, 18. , 5.625 , -180. , 149.0625, 84. , -47.8125, -123.75 , 53.4375, -171.5625, -143.4375, 78. , 95.625 , 42. , 67.5 , -135. , 165.9375, -87.1875, -25.3125, -101.25 , -33.75 , 22.5 , -140.625 , -137.8125, -50.625 , -30. , 92.8125, 174. , 0. , 109.6875, -123.75 , -118.125 , 16.875 , -180. , -95.625 , -56.25 , -33.75 , -106.875 , -140.625 , 19.6875, 120.9375, -151.875 , 140.625 , 16.875 , -60. , -101.25 , 70.3125, -61.875 , -129.375 , -126. , 120. , 101.25 , -70.3125]) - node_lat(n_node)float6444.99 38.68 41.81 ... 41.81 38.68
- standard_name :
- latitude
- long name :
- Latitude of the corner nodes of each face
- units :
- degrees_north
array([44.99388015, 38.68218745, 41.8103149 , 38.68218745, 41.8103149 , 44.99388015, 38.68218745, 44.99388015, 41.8103149 , 38.68218745, 44.99388015, 38.68218745, 44.99388015, 41.8103149 , 38.68218745, 38.68218745, 35.68533471, 35.68533471, 41.8103149 , 38.68218745, 41.8103149 , 35.68533471, 35.68533471, 35.68533471, 35.68533471, 35.68533471, 35.68533471, 44.99388015, 41.8103149 , 44.99388015, 44.99388015, 38.68218745, 35.68533471, 41.8103149 , 44.99388015, 35.68533471, 38.68218745, 41.8103149 , 38.68218745, 38.68218745, 44.99388015, 35.68533471, 44.99388015, 35.68533471, 44.99388015, 38.68218745, 44.99388015, 38.68218745, 38.68218745, 35.68533471, 38.68218745, 35.68533471, 35.68533471, 35.68533471, 35.68533471, 41.8103149 , 44.99388015, 44.99388015, 35.68533471, 44.99388015, 41.8103149 , 38.68218745, 44.99388015, 35.68533471, 38.68218745, 41.8103149 , 35.68533471, 41.8103149 , 35.68533471, 44.99388015, 35.68533471, 35.68533471, 38.68218745, 35.68533471, 38.68218745, 41.8103149 , 35.68533471, 41.8103149 , 38.68218745, 38.68218745, 38.68218745, 38.68218745, 38.68218745, 35.68533471, 44.99388015, 44.99388015, 38.68218745, 38.68218745, 35.68533471, 35.68533471, 44.99388015, 41.8103149 , 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 35.68533471, 38.68218745, 38.68218745, 35.68533471, ... 41.8103149 , 35.68533471, 35.68533471, 35.68533471, 44.99388015, 44.99388015, 38.68218745, 44.99388015, 41.8103149 , 41.8103149 , 38.68218745, 44.99388015, 38.68218745, 41.8103149 , 41.8103149 , 35.68533471, 41.8103149 , 44.99388015, 44.99388015, 38.68218745, 35.68533471, 35.68533471, 38.68218745, 41.8103149 , 35.68533471, 35.68533471, 38.68218745, 44.99388015, 44.99388015, 38.68218745, 41.8103149 , 44.99388015, 35.68533471, 41.8103149 , 44.99388015, 41.8103149 , 41.8103149 , 35.68533471, 44.99388015, 35.68533471, 41.8103149 , 44.99388015, 38.68218745, 38.68218745, 38.68218745, 44.99388015, 35.68533471, 44.99388015, 38.68218745, 44.99388015, 38.68218745, 35.68533471, 38.68218745, 38.68218745, 38.68218745, 44.99388015, 35.68533471, 44.99388015, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 41.8103149 , 35.68533471, 38.68218745, 41.8103149 , 44.99388015, 38.68218745, 44.99388015, 41.8103149 , 38.68218745, 41.8103149 , 35.68533471, 35.68533471, 35.68533471, 41.8103149 , 35.68533471, 35.68533471, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 35.68533471, 41.8103149 , 41.8103149 , 44.99388015, 35.68533471, 38.68218745, 41.8103149 , 41.8103149 , 44.99388015, 44.99388015, 41.8103149 , 38.68218745]) - face_lon(n_face)float6445.0 67.5 78.75 ... -180.0 -90.0
- standard_name :
- longitude
- long name :
- Longitude of the center of each face
- units :
- degrees_east
array([ 45. , 67.5 , 78.75 , 84.375 , 87.1875, 81.5625, 73.125 , 75.9375, 70.3125, 56.25 , 61.875 , 64.6875, 59.0625, 50.625 , 53.4375, 47.8125, 22.5 , 33.75 , 39.375 , 42.1875, 36.5625, 28.125 , 30.9375, 25.3125, 11.25 , 16.875 , 19.6875, 14.0625, 5.625 , 8.4375, 2.8125, 135. , 157.5 , 168.75 , 174.375 , 177.1875, 171.5625, 163.125 , 165.9375, 160.3125, 146.25 , 151.875 , 154.6875, 149.0625, 140.625 , 143.4375, 137.8125, 112.5 , 123.75 , 129.375 , 132.1875, 126.5625, 118.125 , 120.9375, 115.3125, 101.25 , 106.875 , 109.6875, 104.0625, 95.625 , 98.4375, 92.8125, -135. , -112.5 , -101.25 , -95.625 , -92.8125, -98.4375, -106.875 , -104.0625, -109.6875, -123.75 , -118.125 , -115.3125, -120.9375, -129.375 , -126.5625, -132.1875, -157.5 , -146.25 , -140.625 , -137.8125, -143.4375, -151.875 , -149.0625, -154.6875, -168.75 , -163.125 , -160.3125, -165.9375, -174.375 , -171.5625, -177.1875, -45. , -22.5 , -11.25 , -5.625 , -2.8125, -8.4375, -16.875 , -14.0625, -19.6875, -33.75 , -28.125 , -25.3125, -30.9375, -39.375 , -36.5625, -42.1875, -67.5 , -56.25 , -50.625 , -47.8125, -53.4375, -61.875 , -59.0625, -64.6875, -78.75 , -73.125 , -70.3125, -75.9375, -84.375 , -81.5625, -87.1875, 0. , 90. , -180. , -90. ]) - face_lat(n_face)float6438.68 38.68 38.68 ... 38.68 38.68
- standard_name :
- latitude
- long name :
- Latitude of the center of each face
- units :
- degrees_north
array([38.68218745, 38.68218745, 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 41.8103149 , 41.8103149 , 38.68218745, 38.68218745, 38.68218745, 38.68218745])
- node_x(n_node)float64-0.2876 -0.4013 ... -0.1454 0.263
- standard_name :
- x
- long name :
- Cartesian x location of the corner nodes of each face
- units :
- meters
array([-2.87636956e-01, -4.01321326e-01, -5.27046277e-01, 1.14541440e-01, 7.30576629e-02, -4.73197324e-01, -4.65017621e-01, -5.72122502e-01, 6.19740858e-01, 7.34992600e-01, -6.46043182e-01, 4.01321326e-01, 6.46043182e-01, 4.56398915e-17, 6.27003678e-01, 3.83034410e-02, 4.51252400e-01, 2.35778754e-01, -2.85235390e-01, 4.65017621e-01, 5.27046277e-01, -5.74335365e-01, -7.96127424e-02, -2.35778754e-01, 7.96127424e-02, -2.35778754e-01, 7.96626035e-01, -3.53591152e-01, -7.31034186e-01, -4.15671329e-01, -5.25538870e-01, 7.57230404e-01, -7.96626035e-01, 7.41766900e-01, 4.73197324e-01, -3.10828060e-01, 7.79684453e-01, -7.30576629e-02, -7.79684453e-01, 1.14541440e-01, 6.12437841e-01, 5.15275073e-01, 2.18531350e-01, -7.77258388e-01, -2.87636956e-01, 7.05676416e-01, 4.33024273e-17, -4.65017621e-01, 1.89676342e-01, 6.75346943e-01, 7.72175666e-01, 7.96127424e-02, -5.15275073e-01, 4.97349187e-17, -6.75346943e-01, -2.16365424e-01, -7.39206795e-02, -2.18531350e-01, -7.16325433e-01, 4.15671329e-01, -6.19740858e-01, -1.89676342e-01, -7.03308286e-01, 3.10828060e-01, 6.69564182e-01, -6.88619146e-01, 6.27864493e-01, -2.85235390e-01, 1.58458771e-01, -6.12437841e-01, 6.27864493e-01, -8.08321739e-01, 7.79684453e-01, 7.96626035e-01, 5.78404787e-01, -3.51358383e-01, 1.58458771e-01, -4.14097602e-01, -7.79684453e-01, -1.14541440e-01, ... -2.18531350e-01, 5.72122502e-01, 4.01321326e-01, -4.51252400e-01, 8.08321739e-01, -3.83034410e-02, 7.30576629e-02, 7.50405317e-01, -7.96626035e-01, -1.14541440e-01, -6.91728674e-01, 7.39206795e-02, -7.05676416e-01, 7.31034186e-01, 4.73197324e-01, -5.15275073e-01, 5.27046277e-01, 3.53591152e-01, 1.45411741e-01, -6.88619146e-01, -5.74335365e-01, 7.03308286e-01, -6.75346943e-01, -6.57345299e-01, 2.18531350e-01, 6.69564182e-01, -3.33760088e-01, -3.33760088e-01, 6.72570339e-01, 8.08321739e-01, -7.07182304e-01, -6.69564182e-01, 7.39206795e-02, 5.24235541e-01, -4.51252400e-01, 4.65017621e-01, -7.72175666e-01, -6.27003678e-01, 1.47031469e-01, -7.96127424e-02, 5.25538870e-01, 2.85235390e-01, -5.27046277e-01, -7.57230404e-01, 3.83034410e-02, 7.05676416e-01, -1.45411741e-01, 6.19740858e-01, 6.88619146e-01, -6.27864493e-01, -5.78404787e-01, 4.72848836e-01, 6.12437841e-01, -3.83034410e-02, -7.03308286e-01, 7.45355992e-01, -2.62984558e-01, -4.14097602e-01, -3.82883921e-01, 7.77258388e-01, -8.12232862e-01, -7.30576629e-02, 4.51252400e-01, 6.75346943e-01, -2.16365424e-01, -5.76167974e-01, 7.34992600e-01, -4.01321326e-01, -7.16325433e-01, -5.76167974e-01, 7.13261214e-01, 3.53591152e-01, -1.58458771e-01, 2.62984558e-01, 3.51358383e-01, -4.72848836e-01, -4.15671329e-01, -3.53591152e-01, -1.45411741e-01, 2.62984558e-01]) - node_y(n_node)float640.646 -0.6696 ... 0.731 -0.735
- standard_name :
- y
- long name :
- Cartesian y location of the corner nodes of each face
- units :
- meters
array([ 6.46043182e-01, -6.69564182e-01, 5.27046277e-01, 7.72175666e-01, -7.41766900e-01, -5.25538870e-01, -6.27003678e-01, -4.15671329e-01, 4.14097602e-01, -2.62984558e-01, 2.87636956e-01, 6.69564182e-01, 2.87636956e-01, -7.45355992e-01, -4.65017621e-01, 7.79684453e-01, 6.75346943e-01, 7.77258388e-01, 6.88619146e-01, -6.27003678e-01, 5.27046277e-01, 5.74335365e-01, -8.08321739e-01, 7.77258388e-01, 8.08321739e-01, -7.77258388e-01, 1.58458771e-01, -6.12437841e-01, -1.45411741e-01, 5.72122502e-01, -4.73197324e-01, -1.89676342e-01, -1.58458771e-01, 7.30576629e-02, 5.25538870e-01, -7.50405317e-01, -3.83034410e-02, 7.41766900e-01, -3.83034410e-02, -7.72175666e-01, 3.53591152e-01, -6.27864493e-01, 6.72570339e-01, -2.35778754e-01, -6.46043182e-01, 3.33760088e-01, 7.07182304e-01, 6.27003678e-01, 7.57230404e-01, 4.51252400e-01, 1.14541440e-01, -8.08321739e-01, -6.27864493e-01, 8.12232862e-01, -4.51252400e-01, 7.13261214e-01, -7.03308286e-01, 6.72570339e-01, 3.82883921e-01, 5.72122502e-01, -4.14097602e-01, 7.57230404e-01, -7.39206795e-02, 7.50405317e-01, -4.01321326e-01, 2.85235390e-01, -5.15275073e-01, -6.88619146e-01, 7.96626035e-01, 3.53591152e-01, 5.15275073e-01, -7.96127424e-02, 3.83034410e-02, -1.58458771e-01, 5.24235541e-01, 6.57345299e-01, -7.96626035e-01, 6.19740858e-01, 3.83034410e-02, 7.72175666e-01, ... -6.72570339e-01, -4.15671329e-01, -6.69564182e-01, 6.75346943e-01, -7.96127424e-02, -7.79684453e-01, 7.41766900e-01, -3.10828060e-01, 1.58458771e-01, -7.72175666e-01, 1.47031469e-01, -7.03308286e-01, 3.33760088e-01, 1.45411741e-01, -5.25538870e-01, 6.27864493e-01, -5.27046277e-01, 6.12437841e-01, -7.31034186e-01, -2.85235390e-01, -5.74335365e-01, -7.39206795e-02, 4.51252400e-01, 3.51358383e-01, -6.72570339e-01, 4.01321326e-01, 7.05676416e-01, -7.05676416e-01, 2.18531350e-01, 7.96127424e-02, -8.66048545e-17, 4.01321326e-01, 7.03308286e-01, -5.78404787e-01, -6.75346943e-01, 6.27003678e-01, -1.14541440e-01, -4.65017621e-01, 6.91728674e-01, 8.08321739e-01, 4.73197324e-01, 6.88619146e-01, -5.27046277e-01, 1.89676342e-01, -7.79684453e-01, -3.33760088e-01, -7.31034186e-01, -4.14097602e-01, 2.85235390e-01, -5.15275073e-01, -5.24235541e-01, -5.76167974e-01, -3.53591152e-01, 7.79684453e-01, 7.39206795e-02, 0.00000000e+00, 7.34992600e-01, -6.19740858e-01, -7.16325433e-01, 2.35778754e-01, -9.94698375e-17, -7.41766900e-01, -6.75346943e-01, -4.51252400e-01, -7.13261214e-01, -4.72848836e-01, 2.62984558e-01, 6.69564182e-01, -3.82883921e-01, 4.72848836e-01, 2.16365424e-01, -6.12437841e-01, -7.96626035e-01, 7.34992600e-01, -6.57345299e-01, -5.76167974e-01, -5.72122502e-01, 6.12437841e-01, 7.31034186e-01, -7.34992600e-01]) - node_z(n_node)float640.707 0.625 0.6667 ... 0.6667 0.625
- standard_name :
- z
- long name :
- Cartesian z location of the corner nodes of each face
- units :
- meters
array([0.70703125, 0.625 , 0.66666667, 0.625 , 0.66666667, 0.70703125, 0.625 , 0.70703125, 0.66666667, 0.625 , 0.70703125, 0.625 , 0.70703125, 0.66666667, 0.625 , 0.625 , 0.58333333, 0.58333333, 0.66666667, 0.625 , 0.66666667, 0.58333333, 0.58333333, 0.58333333, 0.58333333, 0.58333333, 0.58333333, 0.70703125, 0.66666667, 0.70703125, 0.70703125, 0.625 , 0.58333333, 0.66666667, 0.70703125, 0.58333333, 0.625 , 0.66666667, 0.625 , 0.625 , 0.70703125, 0.58333333, 0.70703125, 0.58333333, 0.70703125, 0.625 , 0.70703125, 0.625 , 0.625 , 0.58333333, 0.625 , 0.58333333, 0.58333333, 0.58333333, 0.58333333, 0.66666667, 0.70703125, 0.70703125, 0.58333333, 0.70703125, 0.66666667, 0.625 , 0.70703125, 0.58333333, 0.625 , 0.66666667, 0.58333333, 0.66666667, 0.58333333, 0.70703125, 0.58333333, 0.58333333, 0.625 , 0.58333333, 0.625 , 0.66666667, 0.58333333, 0.66666667, 0.625 , 0.625 , 0.625 , 0.625 , 0.625 , 0.58333333, 0.70703125, 0.70703125, 0.625 , 0.625 , 0.58333333, 0.58333333, 0.70703125, 0.66666667, 0.66666667, 0.66666667, 0.625 , 0.66666667, 0.58333333, 0.625 , 0.625 , 0.58333333, ... 0.66666667, 0.58333333, 0.58333333, 0.58333333, 0.70703125, 0.70703125, 0.625 , 0.70703125, 0.66666667, 0.66666667, 0.625 , 0.70703125, 0.625 , 0.66666667, 0.66666667, 0.58333333, 0.66666667, 0.70703125, 0.70703125, 0.625 , 0.58333333, 0.58333333, 0.625 , 0.66666667, 0.58333333, 0.58333333, 0.625 , 0.70703125, 0.70703125, 0.625 , 0.66666667, 0.70703125, 0.58333333, 0.66666667, 0.70703125, 0.66666667, 0.66666667, 0.58333333, 0.70703125, 0.58333333, 0.66666667, 0.70703125, 0.625 , 0.625 , 0.625 , 0.70703125, 0.58333333, 0.70703125, 0.625 , 0.70703125, 0.625 , 0.58333333, 0.625 , 0.625 , 0.625 , 0.70703125, 0.58333333, 0.70703125, 0.66666667, 0.66666667, 0.625 , 0.625 , 0.625 , 0.66666667, 0.66666667, 0.66666667, 0.58333333, 0.625 , 0.66666667, 0.70703125, 0.625 , 0.70703125, 0.66666667, 0.625 , 0.66666667, 0.58333333, 0.58333333, 0.58333333, 0.66666667, 0.58333333, 0.58333333, 0.66666667, 0.66666667, 0.625 , 0.625 , 0.58333333, 0.66666667, 0.66666667, 0.70703125, 0.58333333, 0.625 , 0.66666667, 0.66666667, 0.70703125, 0.70703125, 0.66666667, 0.625 ])
- face_node_connectivity(n_face, n_max_face_nodes)int6497 20 74 118 245 ... 216 13 177 128
- cf_role :
- face_node_connectivity
- long name :
- Maps every face to its corner nodes.
- start_index :
- 0
- _FillValue :
- -9223372036854775808
array([[ 97, 20, 74, 118], [245, 213, 105, 63], [ 3, 145, 48, 68], [ 15, 178, 3, 24], [155, 46, 178, 15], [178, 204, 145, 3], [ 48, 122, 245, 17], [145, 210, 122, 48], [122, 42, 213, 245], [ 11, 109, 207, 16], [105, 107, 11, 141], [213, 131, 107, 105], [107, 189, 109, 11], [207, 120, 97, 156], [109, 59, 120, 207], [120, 34, 20, 97], [ 45, 220, 238, 135], [ 80, 8, 197, 49], [ 74, 106, 80, 70], [ 20, 212, 106, 74], ... [ 93, 152, 188, 161], [ 81, 154, 251, 157], [ 19, 95, 174, 234], [205, 223, 19, 41], [188, 186, 223, 205], [223, 85, 95, 19], [174, 246, 81, 96], [ 95, 243, 246, 174], [246, 90, 154, 81], [ 86, 190, 39, 76], [251, 91, 86, 108], [154, 196, 91, 251], [ 91, 147, 190, 86], [ 39, 4, 216, 51], [190, 183, 4, 39], [ 4, 119, 13, 216], [ 72, 227, 36, 133], [225, 155, 15, 53], [ 38, 163, 78, 232], [216, 13, 177, 128]])
- n_nodes_per_face(n_face)int644 4 4 4 4 4 4 4 ... 4 4 4 4 4 4 4 4
- cf_role :
- n_nodes_per_face
- long name :
- Number of nodes per face
array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4])
- zoom :
- 4
- n_side :
- 16
- n_pix :
- 3072
- nest :
- True
- source_grid_spec :
- HEALPix
import geoviews.feature as gf
uxda_lat.isel(time=0).plot(
rasterize=False,
projection=projection,
global_extent=True,
cmap="inferno",
clim=(220, 310),
features=["coastline"],
title=f"Global temperature cross-section at {boulder_lat} degrees latitude",
width=700,
) * gf.grid(projection=projection)
/home/runner/miniconda3/envs/healpix-cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_graticules_30.zip
warnings.warn(f'Downloading: {url}', DownloadWarning)
WARNING:param.GeoOverlayPlot00713: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
Let’s also look at the mean of the cross-section:
print(f"Mean at {boulder_lat} degrees lat (Boulder, CO, USA): {uxda_lat.mean().values} K")
Mean at 40.019 degrees lat (Boulder, CO, USA): 286.6632080078125 K
Latitude interval
uxda_lat_interval = uxda_coarse.cross_section.constant_latitude_interval([boulder_lat-15, boulder_lat+15])
uxda_lat_interval.isel(time=0).plot(
rasterize=False,
projection=projection,
global_extent=True,
cmap="inferno",
clim=(220, 310),
features=["coastline"],
title=f"Global temperature cross-section at the latitude interval [{boulder_lat-5},{boulder_lat+5}] degrees",
width=700,
) * gf.grid(projection=projection)
WARNING:param.GeoOverlayPlot00937: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
print(f"Mean at the latitude interval of [{boulder_lat-5},{boulder_lat+5}] degrees (-/+15 degrees Boulder, CO, USA): {uxda_lat_interval.mean().values} K")
Mean at the latitude interval of [35.019,45.019] degrees (-/+15 degrees Boulder, CO, USA): 286.2819519042969 K
Non-conservative Zonal mean
Calculating the zonal mean is easy by providing the latitude range between -90 and 90 degrees with a step size in degrees:
zonal_mean_tas = uxda.isel(time=0).zonal_mean(lat=(-90, 90, 5))
(
uxda.isel(time=0).plot(
cmap="inferno",
# periodic_elements="split",
height=300,
width=600,
colorbar=False,
ylim=(-90, 90),
)
+ zonal_mean_tas.plot.line(
x="tas_zonal_mean",
y="latitudes",
height=300,
width=180,
ylabel="",
ylim=(-90, 90),
xlim=(220, 310),
# xticks=[220, 250, 280, 310],
yticks=[-90, -45, 0, 45, 90],
grid=True,
)
).opts(title="Temperature and its Zonal means at every 5 degrees latitude")
Remapping
Now, we will be looking into a remapping case. The data set we are using in this section has the zoom=10 available but not for all the variables, e.g. tas has all NaN values at that zoom level. Let us try to remap our data here that is sampled at zoom=9 to a Grid at zoom=10.
Let’s start with creating the destination uxarray.Grid:
%%time
uxgrid_zoom10 = ux.Grid.from_healpix(zoom=10, pixels_only=False)
CPU times: user 20.5 s, sys: 4.58 s, total: 25.1 s
Wall time: 10.4 s
%%time
uxda_remapped = uxda.isel(time=0).remap.inverse_distance_weighted(
uxgrid_zoom10, k=3, remap_to="face centers"
)
CPU times: user 8min 51s, sys: 752 ms, total: 8min 51s
Wall time: 8min 52s
%%time
uxda_remapped.plot(
projection=projection,
cmap="inferno",
features=["borders", "coastline"],
title="Global temperature - remapped to zoom=10",
width=700,
)
CPU times: user 35.6 s, sys: 1.81 s, total: 37.5 s
Wall time: 37.8 s
WARNING:param.GeoOverlayPlot01467: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.